Clustering is a long-standing important research problem, however, remains challenging
when handling large-scale image data from diverse sources. In this paper, we present a …

Free-hand sketch-based image retrieval (SBIR) is a specific cross-view retrieval task, in
which queries are abstract and ambiguous sketches while the retrieval database is formed …

Despite its great success, matrix factorization based cross-modality hashing suffers from two
problems: 1) there is no engagement between feature learning and binarization; and 2) most …

Recent studies show that large-scale sketch-based image retrieval (SBIR) can be efficiently
tackled by cross-modal binary representation learning methods, where Hamming distance …

Multimodal hashing approaches have gained great success on large-scale cross-modal
similarity search applications, due to their appealing computation and storage efficiency …

Sufficient training examples are the fundamental requirement for most of the learning tasks.
However, collecting well-labelled training examples is costly. Inspired by Zero-shot Learning …

Zero-shot hashing (ZSH) aims at learning a hashing model that is trained only by instances
from seen categories but can generate well to those of unseen categories. Typically, it is …

In order to achieve efficient similarity searching, hash functions are designed to encode
images into low-dimensional binary codes with the constraint that similar features will have a …

Learning to hash is regarded as an efficient approach for image retrieval and many other big-
data applications. Recently, deep learning frameworks are adopted for image hashing …

With the dramatic development of the Internet, how to exploit large-scale retrieval techniques
for multimodal web data has become one of the most popular but challenging problems in …